Abstract

Increasingly, researchers and developers of knowledge based systems (KBS) have
been attempting to incorporate the notion of context. For instance, Repertory Grids,
Formal Concept Analysis (FCA) and Ripple-Down Rules (RDR) all integrate either
implicit or explicit contextual information. However, these methodologies treat
context as a static entity, neglecting many connectionists’ work in learning hidden
and dynamic contexts. This thesis argues that the omission of these higher forms of
context, which allow connectionist systems to generalise effectively, is one of the
fundamental problems in the application and interpretation of symbolic knowledge.
This thesis tackles the problems of KBSs by addressing these contextual
inadequacies over a three stage approach: philosophically, methodologically and
through the application of prudence analysis. Firstly, it challenges existing notions
of knowledge by introducing a new philosophical view referred to as Intermediate
Situation Cognition. This new position builds on the existing SC premise, that
knowledge and memory is re-constructed at the moment required, by allowing for
the inclusion of hidden and dynamic contexts in symbolic reasoning.
This philosophical position has been incorporated into the development of a
hybridised methodology, combining Multiple Classification Ripple-Down Rules
(MCRDR) with a function-fitting technique. This approach, referred to as Rated
MCRDR (RM), retains a symbolic core acting as a contextually static memory,
while using a connection based approach to learn a deeper understanding of the
knowledge captured. This analysis of the knowledge map is performed dynamically,
providing constant online information. Results indicate that the method developed
can learn the information that experts have difficulty providing. This supplies the
information required to allow for generalisation of the knowledge captured.
In order to show that hidden and dynamic contextual information can improve the
robustness of a KBS, RM must reduce brittleness. Brittleness, which is widely
recognised as the primary impediment in KBS performance, is caused by a system’s
inability to realise when its knowledge base is inadequate for a particular situation.
RM partly addresses this through providing better generalisation; however,
brittleness can be more directly addressed by detecting when such inadequacies
occur. This process is commonly referred to as prudence analysis. The final part of
this thesis proves the methods philosophical and methodological approach by
illustrating how RM’s use of hidden and dynamic contextual information, allows
the system to perform this analysis. Results show how experts can confidently leave
the verification of cases when not warned, reducing brittleness and the knowledge
acquisition effort.
This thesis shows that the idea of incorporating higher forms of context in symbolic
reasoning domains is both possible and highly effective, vastly improving the
robustness of the KBS approach. Not only does this facilitate improved
classification through better generalisation, but also reduces the KA effort required
by experts. Additionally, the methodology developed has further potential for many
possible applications across numerous domains, such as Information Filtering, Data
Mining, incremental induction and even reinforcement learning.